Puerta Patricia, Ciannelli Lorenzo, Johnson Bethany
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA.
Instituto Español de Oceanografía, Centro Oceanográfico de Baleares, Palma de Mallorca, Spain.
PeerJ. 2019 Feb 25;7:e6471. doi: 10.7717/peerj.6471. eCollection 2019.
Selecting an appropriate and efficient sampling strategy in biological surveys is a major concern in ecological research, particularly when the population abundance and individual traits of the sampled population are highly structured over space. Multi-stage sampling designs typically present sampling sites as primary units. However, to collect trait data, such as age or maturity, only a sub-sample of individuals collected in the sampling site is retained. Therefore, not only the sampling design, but also the sub-sampling strategy can have a major impact on important population estimates, commonly used as reference points for management and conservation. We developed a simulation framework to evaluate sub-sampling strategies from multi-stage biological surveys. Specifically, we compare quantitatively precision and bias of the population estimates obtained using two common but contrasting sub-sampling strategies: the random and the stratified designs. The sub-sampling strategy evaluation was applied to age data collection of a virtual fish population that has the same statistical and biological characteristics of the Eastern Bering Sea population of Pacific cod. The simulation scheme allowed us to incorporate contributions of several sources of error and to analyze the sensitivity of the different strategies in the population estimates. We found that, on average across all scenarios tested, the main differences between sub-sampling designs arise from the inability of the stratified design to reproduce spatial patterns of the individual traits. However, differences between the sub-sampling strategies in other population estimates may be small, particularly when large sub-sample sizes are used. On isolated scenarios (representative of specific environmental or demographic conditions), the random sub-sampling provided better precision in all population estimates analyzed. The sensitivity analysis revealed the important contribution of spatial autocorrelation in the error of population trait estimates, regardless of the sub-sampling design. This framework will be a useful tool for monitoring and assessment of natural populations with spatially structured traits in multi-stage sampling designs.
在生物学调查中选择合适且高效的抽样策略是生态研究中的一个主要关注点，尤其是当抽样种群的数量丰度和个体特征在空间上具有高度结构化时。多阶段抽样设计通常将抽样地点作为主要单元。然而，为了收集诸如年龄或成熟度等特征数据，仅保留在抽样地点收集的个体的一个子样本。因此，不仅抽样设计，而且子抽样策略都可能对重要的种群估计产生重大影响，这些估计通常用作管理和保护的参考点。我们开发了一个模拟框架来评估多阶段生物学调查中的子抽样策略。具体而言，我们定量比较了使用两种常见但截然不同的子抽样策略（随机设计和分层设计）获得的种群估计的精度和偏差。子抽样策略评估应用于一个虚拟鱼类种群的年龄数据收集，该种群具有与东白令海太平洋鳕鱼种群相同的统计和生物学特征。该模拟方案使我们能够纳入多种误差来源的贡献，并分析不同策略在种群估计中的敏感性。我们发现，在所有测试场景中，平均而言，子抽样设计之间的主要差异源于分层设计无法重现个体特征的空间模式。然而，在其他种群估计中，子抽样策略之间的差异可能很小，特别是当使用大子样本量时。在孤立的场景（代表特定的环境或人口统计条件）中，随机子抽样在所有分析的种群估计中提供了更好的精度。敏感性分析揭示了空间自相关在种群特征估计误差中的重要贡献，无论子抽样设计如何。这个框架将是一个有用的工具，用于在多阶段抽样设计中监测和评估具有空间结构化特征的自然种群。
